来宝网 2012/3/22点击1885次
Introduction
Owing to historic inefficiency of mass random bioscreening, the current paradigm suggests that target-specific and pharmacokinetic properties of small molecule libraries should be addressed as early as possible in the discovery process. Computational medicinal chemistry can address this problem at the level of pre-synthetic library design. A number of advanced in silico methods have recently been developed and applied to combinatorial templates to enhance their target-specific informational content. Appropriate strategies for the design of combinatorial libraries are developed in accordance with the target, disease area, resources on hand and the specific project goals.
In this description, we present a rational, practical approach to the design of GPCR-targeted combinatorial library. The goal of the combinatorial synthesis planning strategy presented here is to construct an algorithm utilizing simple, automated procedures for designing combinatorial libraries that are expected to show GPCR-activity.
1. GPCRs as promising drug target
2. Neural networks in the design of GPCR-targeted library
2.1. Unsupervised Kohonen-based learning approach
3. Concept and Applications
3.1. GPCR-related reference database
3.2. Molecular descriptors
3.3. Kohonen map generation
3.4. Rational design of GPCR-specific combinatorial libraries based on the concept of privileged substructures
3.5. Privileged versus peripheral retrosynthetic fragments
3.6. Peripheral retrosynthetic fragments: How to measure the target-specific differences?
3.7. Selection of Building Blocks
Conclusion
Primary bioscreening of large exploratory libraries of small molecules produced by combinatorial synthesis remains a key element of modern drug discovery. The problem of enhancement of bioscreening effectiveness necessitates more serious attention to the quality of screening compound libraries. In this context, advanced cheminformatics technologies, aiming at selection of the proper screening candidates, are of great industrial demand. The further evolution of such technologies will result in the development of integrated cheminformatics platforms, where all the issues related to selection of a rational pharmaceutically relevant screening candidate, having good synthetic feasibility, a desirable profile of target-specific action, drug-likeness, unexploited IP position, favorable ADME/Tox profile, compatibility with assay protocol, etc., will be solved with maximal quality, time- and cost-effectiveness.
The computational algorithm described here is very useful in constraining the size of virtual libraries of potential GPCR active agents. It can be effectively applied as an in silico filter to assist in the product-based design and planning of novel combinatorial libraries. Commonly, the described methodology can be generalized to aid in the selection of an optimal methodology for any arbitrary target-specific library design; it is not restricted to the GPCR-targeted libraries studied here. In addition, the results can be used for profiling the bioactivity of compounds based on comparison with the structures of known agents possessing a certain biological activity. The developed approach combines reagent- and product-based selection procedures, and results in generation of a compact virtual compound library (in the general case, several thousand compounds) targeted against a particular GPCR target. Usually such a library consists of several tens of distinct medium-sized combinatorial sub-libraries (50-200 compounds each) rich in target-specific structural motifs and possessing optimized physico-chemical properties. Such libraries represent a very useful tool at early stages of drug discovery and development, as a valuable source of primary hits easily amenable to further hit-to-lead optimization. Examples of particular GPCR-, protein kinase- and ion channel-targeted libraries generated using the described strategy can be found among the commercial products currently available at Chemical Diversity Labs, Inc.
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